Methods and arrangements for memory-efficient estimation of noise floor

ABSTRACT

A method for noise rise estimation in a wireless communications system is presented, which comprises measuring of samples of at least received total wideband power. From the measured samples of at least received total wideband power, a probability distribution for a first power quantity is estimated. This first power quantity can be the received total wideband power itself. The probability distribution for the first power quantity is used for computing a conditional probability distribution of a noise floor measure. This computing is performed recursively. A value of a noise rise measure is finally calculated based on the conditional probability distribution for the noise floor measure. A node of a wireless communications system having the above functionality is also presented. Typically, the node is a RNC.

TECHNICAL FIELD

The present invention relates in general to methods and devices forestimation of power-related quantities in cellular communicationssystems, and in particular for estimation of noise floor.

BACKGROUND

Wideband Code Division Multiple Access (WCDMA) telecommunication systemshave many attractive properties that can be used for future developmentof telecommunication services. A specific technical challenge in e.g.WCDMA and similar systems is the scheduling of enhanced uplink channelsto time intervals where the interference conditions are favourable, andwhere there exist a sufficient capacity in the uplink of the cell inquestion to support enhanced uplink channels. It is well known thatexisting users of the cell all contribute to the interference level inthe uplink of WCDMA systems. Further, terminals in neighbour cells alsocontribute to the same interference level. This is because all users andcommon channels of a cell transmit in the same frequency band when CDMAtechnology is used. The load of the cell is directly related to theinterference level of the same cell. The admission control function ofthe RNC in WCDMA is thus central, since overload results in poor qualityof service and unstable cells, behaviors needed to be avoided.

The present invention relates to the field of load estimation in codedivision multiple access cellular telephone systems. Several radioresource management (RRM) algorithms such as scheduling and admissioncontrol rely on accurate estimates of the uplink load.

The admission control algorithms need to balance the available resourcesof each cell or RBS, against the requested traffic by users. This meansthat important inputs to the admission control algorithms includeavailable HW resources, as well as information on the momentary numberof users and their ongoing traffic, in each cell.

In order to retain stability of a cell and to increase the capacity,fast enhanced uplink scheduling algorithms operate to maintain the loadbelow a certain level. This follows since the majority of uplink userchannels, at least in WCDMA, are subject to power control. This powercontrol aims at keeping the received power level of each channel at acertain signal to interference ratio (SIR), in order to be able to meetspecific service requirements. This SIR level is normally such that thereceived powers in the radio base station (RBS) are several dBs belowthe interference level. De-spreading in so called RAKE-receivers thenenhance each channel to a signal level where the transmitted bits can befurther processed, e.g. by channel decoders and speech codecs that arelocated later in the signal processing chain.

Since the RBS tries to keep each channel at its specific preferred SIRvalue, it may happen that an additional user, or bursty data traffic ofan existing user, raises the interference level, thereby momentarilyreducing the SIR for the other users. The response of the RBS is tocommand a power increase to all other users, something that increasesthe interference even more. Normally this process remains stable below acertain load level. In case a high capacity channel would suddenlyappear, the raise in the interference becomes large and the risk forinstability, a so called power rush, increases. It is thus a necessityto schedule high capacity uplink channels, like the enhanced uplink(E-UL) channel in WCDMA, so that one can insure that instability isavoided. In order to do so, the momentary load must be estimated in theRBS. This enables the assessment of the capacity margin that is left tothe instability point.

A particularly useful measure is the uplink (and downlink) cell load(s),measured in terms of the rise over thermal (or noise rise). Rise overthermal (ROT) is defined as the quotient between the momentary wide bandpower and a thermal noise floor level. All noise rise measures have incommon that they rely on accurate estimates of the background noise.Determinations of highly fluctuating power quantities or noise flooraccording to prior art is typically associated with relatively largeuncertainties, which even may be in the same order of magnitude as theentire available capacity margin. It will thus be very difficult indeedto implement enhanced uplink channel functionality without improving theload estimation connected thereto.

At this point it could be mentioned that an equally important parameterthat requires load estimation for its control, is the coverage of thecell. The coverage is normally related to a specific service that needsto operate at a specific SIR to function normally. The uplink cellboundary is then defined by a terminal that operates at maximum outputpower. The maximum received channel power in the RBS is defined by themaximum power of the terminal and the pathloss to the digital receiver.Since the pathloss is a direct function of the distance between theterminal and the RBS, a maximum distance from the RBS results. Thisdistance, taken in all directions from the RBS, defines the coverage.

It now follows that any increase of the interference level results in areduced SIR that cannot be compensated for by an increased terminalpower. As a consequence, the pathloss needs to be reduced to maintainthe service. This means that the terminal needs to move closer to theRBS, i.e. the coverage of the cell is reduced.

From the above discussion it is clear that in order to maintain the cellcoverage that the operator has planned for, it is necessary to keep theload below a specific level. This means that load estimation isimportant also for coverage. In particular load estimation is importantfrom a coverage point of view in the fast scheduling of enhanced uplinktraffic in the RBS.

Furthermore, the admission control and congestion control functionalityin the radio network controller (RNC) that controls a number of RBSsalso benefits from accurate information on the momentary noise rise ofeach cell it controls. The bandwidth by which the RNC functionalityaffect the cell performance is significantly slower than what wasdescribed above, for enhanced uplink scheduling, however, the impacts oncell stability that was discussed above for enhanced uplink are alsovalid to some extent for the admission control functionality of the RNC.

Admission control assures that the number of users in a cell do notbecome larger than what can be handled, in terms of hardware resourcesand in terms of load. A too high load first manifests itself in too poorquality of service, a fact that is handled by the outer power controlloop by an increase of the SIR target. In principle this feedback loopmay also introduce power rushes, as described above.

The admission control function can prevent both the above effects byregulation of the number of users and corresponding types of trafficthat is allowed for each cell controlled by the RNC. A particularlyimportant input to achieve this goal is an accurate estimate of thenoise rise of the cell.

Even though noise rise estimated in the RBS may be signaled to the RNC,all vendors may not support this signaling, or may not provide accurateenough load estimation. Hence there is a need for estimation of noiserise in the RNC.

An additional problem appears when scheduling of enhanced uplink trafficis implemented in RNCs. Since the RNC may control about 1000 cells todayand probably far more in the future, also quite moderate requirementsconcerning memory consumption and processing power algorithms for noiserise estimation multiplies with the number of served cells. Inparticular memory wasting solutions are difficult to implement in RNCs.A final very important advantage is that the algorithm disclosed in thepresent invention disclosure lends itself to ASIC implementation.

SUMMARY

A general problem with prior art CDMA communications networks is thatload estimations are presented with an accuracy which makes careful loadcontrol difficult. In particular, determination of noise rise inconnection with enhanced uplink channels, suffers from largeuncertainties, primarily caused by difficulties to estimate the noisefloor or other power-related quantities. Furthermore, high memoryrequirements during noise floor estimations may be an additionalobstacle.

A general object of the present invention is to provide improved methodsand arrangements for determining power-related quantities, e.g. loadestimation. A further object of the present invention is to providemethods and arrangements giving more accurate determination ofpower-related quantities. Yet a further object of the present inventionis to provide methods and arrangements for improving noise riseestimations. Another object of the present invention is to providemethods and arrangements for determining power-related quantities havinglow requirements on memory.

The above objects are achieved with methods and devices according to theenclosed patent claims. In general words, in a first aspect, a methodfor noise rise estimation in a wireless communications system ispresented, comprising measuring of samples of at least received totalwideband power. From the measured samples of at least received totalwideband power, a probability distribution for a first power quantity isestimated. Typically, this first power quantity is the received totalwideband power itself. The probability distribution for the first powerquantity is used for computing a conditional probability distribution ofa noise floor measure. This computing is performed recursively. A valueof a noise rise measure is finally calculated based on the conditionalprobability distribution for the noise floor measure.

In a second aspect a node of a wireless communications system ispresented. Typically, the node is a RNC. The node comprises means forobtaining measured samples of at least received total wideband power andmeans for estimating a probability distribution for a first powerquantity from at least the measured samples of at least received totalwideband power. The node further comprises means, operating in arecursive manner, for computing a conditional probability distributionof a noise floor measure based on at least said probability distributionfor a first power quantity. The node also comprises means forcalculating a value of the noise rise measure based on the conditionalprobability distribution for the noise floor measure.

One advantage of the present invention is that an accurate noise risevalue can be provided, even in the presence of neighbour cellinterference, external interference sources and rapidly fluctuatingpowers. Furthermore, the present invention has a comparatively lowcomputational complexity and memory requirements. Further advantages arediscussed in connection with the detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention, together with further objects and advantages thereof, maybest be understood by making reference to the following descriptiontaken together with the accompanying drawings, in which:

FIG. 1 shows a signal chain of a radio base station performing loadestimation;

FIG. 2 illustrates a typical relation between noise rise and totalbitrate in a cell;

FIG. 3 is a schematic illustration of signal powers occurring in atypical mobile communications network;

FIG. 4 is a time diagram of received total wideband power;

FIG. 5 is a block scheme of an embodiment of a noise rise estimationarrangement according to the present invention;

FIG. 6 is an illustration of interdependent recursive algorithmsaccording to the present invention;

FIG. 7 is a diagram illustrating tracking behaviour of a simulationaccording to the present invention;

FIG. 8 is a diagram illustrating tracking behaviour at a sudden changeof background level;

FIG. 9 is a diagram illustrating a probability density function of aminimum of a power quantity derived from total received powermeasurements;

FIG. 10 is a block diagram of main parts of an embodiment of a systemaccording to the present invention; and

FIG. 11 is flow diagram of main steps of an embodiment of a methodaccording to the present invention.

DETAILED DESCRIPTION

Throughout the entire disclosure, bold letters in equations refer tovector or matrix quantities.

In the present disclosure, complements to different distributionfunctions are discussed. The definition follows. A complement to acumulative distribution function F is defined as one minus thecumulative distribution function F. In the case of e.g. a cumulativeerror distribution function F_(Δx(t′|t′))(x−{circumflex over (x)}_(P)_(Total) ^(Kalman)(t′|t′)) (defined further below), the complement ofthe cumulative error distribution function becomes1−F_(Δx(t′|t′))(x−{circumflex over (x)}_(P) _(Total) ^(Kalman)(t′|t′)).

The present detailed description is introduced by a somewhat deeperdiscussion about how to perform load estimation and the problemsencountered by prior art solutions, in order to reveal the seriousnessthereof. This is done with reference to a typical WCDMA system, but theideas are not restricted to WCDMA. They are rather applicable in manytypes of cellular systems, in particular all sorts of CDMA systems.

Reference and Measurement Points

A typical signal chain of a RBS is depicted in FIG. 1. A receivedwideband signal from an antenna 1 first passes an analogue signalconditioning chain 2, which consists of cables, filters etc. Variationsamong components together with temperature drift, render the scalefactor of this part of the system to be undetermined with about 2-3 dBs,when the signal enters a receiver 3. This is discussed further below. Inthe receiver 3, a number of operations take place. For load estimationit is normally assumed that a total received wideband power is measuredat some stage, in FIG. 1 denoted by 5. Furthermore, it is assumed inthis embodiment that code power measurements, i.e. powers of eachindividual channel/user of the cell, are made available at a stage 6. Areference point for estimated quantities is referred to as 4. The pointsin the chain where estimated quantities are valid, and wheremeasurements are taken, are schematically shown in FIG. 1.

There are several reasons for the difficulties to estimate the thermalnoise floor power. One reason as indicated above is that the thermalnoise floor power, as well as the other received powers, is affected bycomponent uncertainties in the analogue receiver front end. The signalreference points are, by definition, at the antenna connector. Themeasurements are however obtained after the analogue signal conditioningchain, in the digital receiver. These uncertainties also possess athermal drift.

The analogue signal conditioning electronics chain does introduce ascale factor error of 2-3 dB between RBSs (batch) that is difficult tocompensate for. The RTWP (Received Total Wideband Power) measurementthat is divided by the default value of the thermal noise power floormay therefore be inconsistent with the assumed thermal noise power floorby 2-3 dB. The effect would be a noise rise estimate that is also wrongby 2-3 dB. Considering the fact that the allowed noise rise interval ina WCDMA system is typically 0-7 dB, an error of 2-3 dB is notacceptable.

Fortunately, all powers forming the total received power are equallyaffected by the scale factor error γ(t) so when the noise rise ratioN_(R)(t) is calculated, the scale factor error is cancelled as

$\begin{matrix}\begin{matrix}{{N_{R}(t)} = {N_{R}^{DigitalReceiver}(t)}} \\{= \frac{P^{{Total},{DigitalReceiver}}(t)}{P_{N}^{DigitalReceiver}}} \\{= {\frac{{\gamma (t)}{P^{{Total},{Antenna}}(t)}}{{\gamma (t)}P_{N}^{Antenna}} =}} \\{= \frac{P^{{Total},{Antenna}}(t)}{P_{N}^{Antenna}}} \\{= {N_{R}^{Antenna}(t)}}\end{matrix} & (1)\end{matrix}$

where N_(R) ^(DigitalReceiver)(t) and N_(R) ^(Antenna)(t) are the noiserise ratios as measured at the digital receiver 3 (FIG. 1) and at theantenna 1 (FIG. 1), respectively, P^(Total,DigitalReceiver)(t) andP^(Total,Antenna)(t) are the total received powers at the digitalreceiver 3 and the antenna 1, respectively, and P_(N) ^(DigitalReceiver)and P_(N) ^(Antenna) are the thermal noise level as measured at thedigital receiver 3 and the antenna 1, respectively. However, note that(1) requires measurement of the noise floor P_(N) ^(DigitalReceiver) inthe digital receiver. This is one difficulty that is addressed by thepresent invention.

Power Measurements

In the detailed description the following general notations are used:

Measurements of the total received wideband power are performed in thereceiver. This measurement is denoted by P^(Total)(t), where t denotesdiscrete time. The measurement rate is T⁻¹ HZ.

Noise Rise

As indicated in the background section, the result of introducingadditional channels becomes an increase of the total power. FIG. 2 is adiagram illustrating these conditions. Noise rise N_(R), defined as theratio between a total power and the thermal noise level P_(N) asmeasured at the antenna connector, also referred to as the noise floor,is a measure of the load. Above a noise rise threshold N_(R) ^(thr), thesituation becomes unstable. A relation 100 between total bit rate andnoise rise N_(R) is known from the design of the control loops, andscheduling of additional channels can be performed once theinstantaneous noise rise N_(R) has been determined. The pole capacity,C_(pole), denotes the maximum bitrate capacity in bits per second. Atypical difference ΔN between the threshold N_(R) ^(thr) and the leveldefined by the thermal noise level P_(N) is 7 dB. However, the noisefloor or thermal noise level P_(N) is not readily available. Forinstance, since scale factor uncertainties in the receiver may be aslarge as 2-3 dB as discussed above, a large part of the available marginis affected by such introduced uncertainties.

Observability of Noise Floor

One reason for the difficulties to estimate the thermal noise floorpower now appears, since even if all measurements are made in thedigital receiver, the noise floor cannot be directly measured, at leastnot in a single RBS. The explanation is that neighbour cell interferenceand interference from external sources also affect the receiver, and anymean value of such sources cannot be separated from the noise floor.Power measurements on the own cell channels may in some cases beperformed, increasing the complexity of the system. Such measurements dohowever not solve the entire problem, although they may improve thesituation somewhat.

FIG. 3 illustrates the contributions to power measurements in connectionwith an RBS 20. The RBS 20 is associated with a cell 30. Within the cell30, a number of mobile terminals 25 are present, which communicates withthe RBS 20 over different links, each contributing to the total receivedpower by P_(i) ^(Code)(t). The cell 30 has a number of neighbouringcells 31 within the same WCDMA system, each associated with a RBS 21.The neighbouring cells also comprise mobile terminals 26. The mobileterminals 26 emit radio frequency power and the sum of all suchcontributions is denoted by P^(N). There may also be other networkexternal sources of radiation, such as e.g. a radar station 41.Contributions from such external sources are denoted by P^(E). Finally,the P_(N) term arises from the receiver itself. The RBS's 20, 21 aretypically connected to a RNC 172.

It is clear from the above that P^(N)(t) and P_(N) are not measurableand hence need to be estimated or eliminated in some way. The situationbecomes even worse if only measurements of total wide band power areavailable. Total wide band power measurement P_(Measurement) ^(Total)(t)can be expressed by:

$\begin{matrix}{{{P_{Measurement}^{Total}(t)} = {{\sum\limits_{t = 1}^{n}{P_{t}^{Code}(t)}} + {P^{E + N}(t)} + {P_{N}(t)} + {^{Total}(t)}}},{where}} & (2) \\{{P^{E + N} = {P^{E} + P^{N}}},} & (3)\end{matrix}$

and where e^(Total)(t) models measurement noise.

It can be mathematically proved that a linear estimation of P^(E+N)(t)and P_(N) is not an observable problem. Only the sum P^(E+N)+P_(N) isobservable from the available measurements. This is true also in casecode power measurements are performed. The problem is that there is noconventional technique that can be used to separate the noise floor frompower mean values originating from neighbour cell interference andin-band interference sources external to the cellular system.Furthermore, if only measurements of total received wide band power isavailable, also the individual code power contributions areindistinguishable from the other contribution.

Noise Floor Estimations

Yet another reason for the difficulty with noise rise estimation is thatthe thermal noise floor is not always the sought quantity. There aresituations where constant in-band interference significantly affects thereceiver of the RBS. These constant interferers do not affect thestability discussed above, they rather appear as an increased noisetemperature, i.e. an increased thermal noise floor.

In prior art, one alternative is to use costly and individualdetermination of the thermal noise floor of each RBS in the field, inorder to achieve a high enough load estimation performance. Theestablishment of the default value for the thermal noise power floor, asseen in the digital receiver requires reference measurements performedover a large number of RBSs either in the factory or in the field. Bothalternatives are costly and need to be repeated as soon as the hardwarechanges.

The above approach to solve the problem would require calibration ofeach RBS individually. This would however be very costly and isextremely unattractive. Furthermore, temperature drift errors in theanalogue signal conditioning electronics of perhaps 0.7-1.0 dB wouldstill remain.

Another approach is to provide an estimation of the thermal noise powerfloor. One principle for estimation of the thermal noise power floor isto estimate it as a minimum of a measured or estimated power quantitycomprising the thermal noise floor. If no code power measurements areavailable, the power quantity in question is typically the totalreceived wideband power. One approach would therefore be to calculatethe noise rise as a division of the momentary total received widebandpower with an established thermal noise floor power estimated as aminimum of the total received wideband power.

This is schematically illustrated in FIG. 4. Momentary values 102 of areceived total wideband power are here illustrated as a function oftime. The values fluctuate considerably depending on the momentary load.It is known that the thermal noise floor contribution always is present,and consequently it can be concluded that if measurement uncertaintiesare neglected, the noise floor contribution has to be equal to orsmaller than the minimum value 104 of the total received wideband powerreceived within a certain period of time. If there is a reasonableprobability that all code power contributions, neighbour cellcontributions and other external contributions at some occasion areequal to zero, the minimum value 104 is a good estimation of the “true”noise floor 106. However, in all situations, it is certain that theminimum value 104 constitutes an upper limit of the unknown noise floor.

In order to improve the estimation of the noise floor, a recursiveestimation filter can be applied to the series of measurements,providing estimates of the received total wideband power, as well as thevariance thereof. The thermal noise power floor may then be estimated bysoft algorithms.

The principle of using a division with an established thermal noisefloor power has a number of properties, some of which may bedisadvantages, at least in certain applications. The estimationprinciple establishes a specific value of the thermal noise power floor,as the output variable. This is neither optimal nor necessary. Theoutput quantity that is really needed is the noise rise, and as will bementioned below, this quantity can be estimated directly. Furthermore,the estimation principle does not provide any measure of the accuracy ofthe estimated thermal noise power floor, nor the noise rise. This is aconsequence of the fact that only one value of the thermal noise powerfloor is estimated.

Moreover, the estimation principle above does not account for priorinformation that is available on e.g. the probability distribution ofthe true thermal noise floor power, over a collection of RBSs. This hasfurther consequences. The estimate of the thermal noise power floorobtained by the above ideas is always biased to be higher than the truevalue. This follows since the sum of thermal noise floor power,neighbour cell WCDMA power and non-WCDMA in-band interference power isalways at least as great as the thermal noise power floor. Hence, whenthe minimum is estimated over a determined interval of time, a valuelarger than the true thermal noise power is always obtained. Aconsequence of this is that the noise rise is underestimated, i.e. theload of the cell is underestimated. The consequence could be tooaggressive scheduling, leading e.g. to cell instability.

Mentioned above, the present invention allows a direct estimation of theconditional probability distribution of the noise rise. This is obtainedas follows. The recursive algorithm proposed in the present inventionestimates the conditional probability distribution of the noise floor.Furthermore the probability distribution of the received total widebandpower is available from processing described further below. From thesetwo probability distributions, the conditional probability distributionof the noise rise is easily computed by considering formulas for thedistribution of the quotient of two random variables.

Thereby an additional important benefit in the present invention is anestimate of the one dimensional probability density function of thenoise rise, not just a single value, or at least a probability densityfunction of the noise floor is determined. An important benefit of thefact that the complete probability distribution is estimated is thepossibility to compute the variance (standard deviation) of theestimate. Thereby the quality of the estimation process willautomatically be assessed. Uncertainty measures like this one are likelyto be highly useful when e.g. enhanced uplink channels are scheduled inlater steps.

A value of the noise floor can indeed be estimated from the conditionalprobability distribution of the noise floor also in the presentinvention. This estimated noise floor value can then be utilised forcalculation of a noise rise measure by simply divide an estimate of apresently received total wideband power with the estimated noise floorvalue in a conventional manner. However, such embodiment does not havethe advantages described above.

An embodiment of estimating noise rise is schematically illustrated as ablock diagram in FIG. 5. This embodiment relates to the field of loadestimation in code division multiple access cellular telephone systems.The disclosure of the preferred embodiment is written for loadestimation functionality with respect to the enhanced uplink (E-UL) inWCDMA type cellular systems. Note however, that the situation for othercellular systems of CDMA type should be similar so most of the detaileddiscussion should be valid for these systems as well.

Note that in the following description, probability distributions arehandled by digital systems, typically by discretising the distributionsas histograms.

A noise rise estimation arrangement 50 comprises three main blocks 60,70, 80. In a first, power estimation block 60, a Kalman filterarrangement receives inputs 61, in the present embodiment the measuredreceived total wideband power RTWP. Mathematical details of preferredembodiment are disclosed in Appendix A. The output 69 from the powerestimation block 60 is the estimate of a power quantity and thecorresponding variance, in the present embodiment the estimate of thereceived total wideband power RTWP and the corresponding variance. Sincethe outputs are from the Kalman filter arrangement, these parameter arethe only ones needed to define the estimated Gaussian distribution thatis produced by the filter. Thus, enough information is given to definethe entire probability distribution information of the RTWP estimate.The filter details are discussed more in detail further below.

In more advanced systems, the power estimation block 60 may base itsestimated on further power parameters 62, e.g. measured code power tointerference ratio (C/I) of different channels i, beta factors for thechannels i, number of codes for the channels i, and corresponding codepower to interference ratio commanded by the fast power control loop. Insuch cases, the output 69 from the power estimation block 60 may be anestimate of another power related quantity and corresponding variance.The estimate of a power quantity could e.g. be the sum of neighbour cellWCDMA interference power, in-band non-WCDMA interference power andthermal noise floor power.

In a second, conditional probability distribution estimation block 70,an arrangement based on Bayesian statistics receives the power quantityestimate and the corresponding standard deviation 69 as inputs, andprovides an output 79 comprising parameters associated with a noisefloor power. This may be a single value of a noise floor power orparameters of an estimated probability distribution of a noise floorpower. Prior known parameters representing histograms of probabilitydensity functions of noise floor is stored in a storage 71 providinginformation 72 about a prior expected probability distribution of thenoise floor power to the conditional probability distribution estimationblock 70, in order to achieve an optimal estimation.

The effect on the subsequent noise power floor estimation processingblock is beneficial, but intricate to understand. A highly technicalexplanation follows for the interested reader.

Note that when the long term average load of the system increases, thennormally the neighbour cell interference increases. The consequence isthat the likelihood of low values of the estimated total power isreduced with increasing neighbour cell interference. The soft noisepower floor estimation algorithm operates by removing portions of theprior probability distribution of the thermal noise power floor, fromabove, by application of a calculation of the probability distributionof the minimum of the estimated total power.

This moves the centre of gravity of the prior distribution towards lowervalues, thereby reducing the optimal estimate of the thermal noise powerfloor. The amount that is cut away is determined by the probabilitydistributions of the estimated total power that fall within apre-determined, sparsely sampled sliding window. Then a total powerprobability distribution with a larger variance will obviously cut awaya larger portion of the prior probability distribution than one with thesame mean value and a smaller variance. The reason is that theprobability distribution function with the larger variance extendsfurther into the region of nonzero support of the prior probabilitydistribution.

A possible straight-forward approach for estimating the minimum is tocompute the estimate over a pre-determined interval of time, a so-calledsliding window. The detailed mathematical description of the estimationof the conditional probability distribution based on such a slidingwindow is given in Appendix B.

In a third, noise rise estimation block 80, the estimated probabilitydistribution of the noise floor 79 and a RTWP estimate and acorresponding standard deviation 68 are received as inputs, and providesprimarily an output 81 comprising a noise rise value. In thisembodiment, the preferred noise rise measure is defined according to:

$\begin{matrix}{{{{RoT}(t)} = \frac{P^{Total}(t)}{P_{N}}},} & (4)\end{matrix}$

where P^(Total)(t) is a received total Sideband power, however, alsoother noise rise measures can be utilized. As mentioned further above,the actual noise rise determination can preferably be performed bydetermining a probability distribution function of a quotient of aprobability distribution function of the RTWP and a probabilitydistribution function of the noise floor.

The blocks 60, 70 and 80 are preferably integrated into one processor.However, any arrangements comprising, but not limited to, differentdistributed solutions are also possible to use, where the processormeans comprising the blocks 60, 70 and 80 may be considered as adistributed processor means.

The estimation of the conditional probability distribution of thethermal noise floor given in Appendix B was based on a sliding window.These algorithms require parameters for management of the sliding windowsize, since the window size affects the computational complexity. Moreimportantly, the algorithms require storage of two matrix variables,together occupying as much as 0.4-0.8 Mbyte of memory. In particular,one probability distribution function and one cumulative distributionfunction needs to be computed on a grid, for each power sample that isstored in the sliding window. Typically the grid is discretized in stepsof 0.1 dB over the range −120 dBm to −70 dBm, resulting in 1000variables, for each power sample in the sliding window. With 100 samplespower samples in the sliding window, the result is a need to store400000-800000 bytes depending on if 4 byte or 8 byte variables are used.

An RNC may control about 1000 cells today and more in the future.Therefore, in the RNC 1000 instances may be needed (one per cell). Thememory consumption then approaches 1 GB of dynamic memory, a fact thatis prohibitive. The conclusion is that the memory consumption of suchalgorithms for soft noise floor estimation is not acceptable, at leastfor an RNC implementation. The memory requirements for sliding windowapproaches would, however, probably be feasible for RBS implementationswhere only 4 instances need to run in parallel (4 diversity branches).

It should also be noted that the actual computational complexity is noproblem since the updates of the noise floor only need to take place afew times per minute, meaning that the noise floor updates for differentcells can be scheduled to different intervals of time.

A second problem is also indirectly related to the use of a slidingwindow for estimation of a minimum. The problem is due to the fact thata power sample with a small value that enters the sliding window remainsthere during the whole duration of the window. During this period, thesmall value naturally dominates the minimum estimate. Hence, in case thenoise floor would start to increase, this is not reflected until thepower sample with a small value finally is shifted out of the slidingwindow.

In order to solve the above problems, in particular the memory problems,and enable load estimation for admission control purposes in the RNC,the present invention instead uses a recursive algorithm for soft noisefloor estimation.

A first main idea in order to find a suitable recursive algorithm is tointroduce approximations in the computation of the probabilitydistribution of the minimum power, i.e. the noise floor estimate.

All notation used in this part of the description is explained in detailin the Appendix B. The reason for this approach is that it is anecessary to read the Appendix B to understand the details of thepresent section. To summarize briefly though, t denotes time, x denotes(discretized) power, ƒ denotes probability density functions and Fdenotes cumulative distribution functions.

The first step towards a recursive formulation is to remove thetransient effect of the sliding window by consideration of the casewhere

T_(log)→∞,   (5)

i.e. where the width of the sliding window becomes infinite.

Then, the key formula (B12) of Appendix B is transformed into:

$\begin{matrix}{{f_{{\min {\{{x_{p^{Total}}^{0}{(t^{\prime})}}\}}_{t^{\prime} \leq t}}|Y^{\prime}}(x)} = {\sum\limits_{t^{\prime} \leq t}{{f_{\Delta \; {x{({t^{\prime}|t})}}}\left( {x - {{\hat{x}}_{P^{Total}}^{Kalman}\left( t^{\prime} \middle| t \right)}} \right)}{\underset{q \neq t^{\prime}}{\prod\limits_{q \leq t}}\; {\left( {1 - {F_{\Delta \; {x{({q|t})}}}\left( {x - {{\hat{x}}_{P^{Total}}^{Kalman}\left( q \middle| t \right)}} \right)}} \right).}}}}} & (6)\end{matrix}$

For the discussion that follows, the update time t is discretized, i.e.a subscript_(N) is introduced to give:

$\begin{matrix}\begin{matrix}{{f_{\min}\left( {t_{N},x} \right)} \equiv {f_{{\min {\{{x_{P^{Total}}^{0}{(t^{\prime})}}\}}_{t^{\prime} \leq t_{N}}}|Y^{t_{N}}}(x)}} \\{= {\sum\limits_{t^{\prime} \leq t_{N}}{f_{\Delta \; {x{({t^{\prime}|t_{N}})}}}\left( {x - {{\hat{x}}_{P^{Total}}^{Kalman}\left( t^{\prime} \middle| t_{N} \right)}} \right)}}} \\{{{\underset{q \neq t^{\prime}}{\prod\limits_{q \leq t_{N}}}\; \left( {1 - {F_{\Delta \; {x{({q|t_{N}})}}}\left( {x - {{\hat{x}}_{P^{Total}}^{Kalman}\left( q \middle| t_{N} \right)}} \right)}} \right)},}}\end{matrix} & (7)\end{matrix}$

where t_(N) is the discretized time of update.

The first approximation to be introduced is obtained by replacement ofthe smoothing estimate {circumflex over (x)}_(P) _(Total)^(Kalman)(t′|t_(N)) by the filter estimate {circumflex over (x)}_(P)_(Total) ^(Kalman)(t′|t′), according to:

Assumption 1: {circumflex over (x)} _(P) _(Total) ^(Kalman)(t′|t_(N))≈{circumflex over (x)} _(P) _(Total) ^(Kalman)(t′|t′).

This assumption means that the smoothing gain is assumed to be small. Inpractice the approximation means that a slightly worse performance isaccepted, to gain computational simplifications. Approximation 1simplifies equation (7) to

$\begin{matrix}{{f_{\min}\left( {t_{N},x} \right)} \approx {\sum\limits_{t^{\prime} \leq t_{N}}\; {{f_{\Delta \; {x{({t^{\prime}t^{\prime}})}}}\left( {x - {{\hat{x}}_{pTotal}^{Kalman}\left( {t^{\prime}t^{\prime}} \right)}} \right)}{\prod\limits_{\underset{q \neq t^{\prime}}{q \leq t_{N}}}\; {\left( {1 - {F_{\Delta \; {x{({qq})}}}\left( {x - {{\hat{x}}_{pTotal}^{Kalman}\left( {qq} \right)}} \right)}} \right).}}}}} & (8)\end{matrix}$

The next step comprises a formulation of a recursive update of acompleted product. The completed product, Γ(t_(N),x) is defined as

$\begin{matrix}{{\Gamma \left( {t_{N},x} \right)} = {\prod\limits_{q \leq t_{N}}\; {\left( {1 - {F_{\Delta \; {x{({qq})}}}\left( {x - {{\hat{x}}_{pTotal}^{Kalman}\left( {qq} \right)}} \right)}} \right).}}} & (9)\end{matrix}$

It then follows that the completed product can be formulated recursivelyby:

$\begin{matrix}\begin{matrix}{{\Gamma \left( {t_{N + 1},x} \right)} = {\prod\limits_{q \leq t_{N + 1}}\left( {1 - {F_{\Delta \; {x{({qq})}}}\left( {x - {{\hat{x}}_{pTotal}^{Kalman}\left( {qq} \right)}} \right)}} \right)}} \\{= \left( {1 - {F_{\Delta \; {x{({t_{N + 1}t_{N + 1}})}}}\left( {x - {{\hat{x}}_{pTotal}^{Kalman}\left( {t_{N + 1}T_{N + 1}} \right)}} \right)}} \right)} \\{{\prod\limits_{q \leq t_{N}}\; \left( {1 - {F_{\Delta \; {x{({qq})}}}\left( {x - {{\hat{x}}_{pTotal}^{Kalman}\left( {qq} \right)}} \right)}} \right)}} \\{= \left( {1 - {F_{\Delta \; {x{({t_{N + 1}t_{N + 1}})}}}\left( {x - {{\hat{x}}_{pTotal}^{Kalman}\left( {t_{N + 1}t_{N + 1}} \right)}} \right)}} \right)} \\{{{\Gamma \left( {t_{N},x} \right)}.}}\end{matrix} & (10)\end{matrix}$

This is the first result, where it is noticed that computing a presentcompleted product Γ(t_(N+1),x), i.e. a product of complements of acumulative error distribution of a first power quantity, can be computedas a product of a previously computed completed product Γ(t_(N),x), i.e.a previously computed product of complements of the cumulative errordistribution of the first power quantity and a first factor 1−F_(Δx(t)_(N+1) _(|t) _(N+1) ₎(x−{circumflex over (x)}_(P) _(Total)^(Kalman)(t_(N+1)|t_(N+1))) based on a new complement, of the cumulativeprobability distribution for the first power quantity.

The next step is to obtain a recursive update of the probability densityfunction of the minimum power itself, i.e. to write ƒ_(min)(t_(N),x)recursively. This is obtained as follows, starting with (8).

$\begin{matrix}\begin{matrix}{{f_{\min}\left( {t_{N + 1},x} \right)} \approx {\sum\limits_{t^{\prime} \leq t_{N + 1}}\; {f_{\Delta \; {x{({t^{\prime}t^{\prime}})}}}\left( {x - {{\hat{x}}_{P^{Total}}^{Kalman}\left( {t^{\prime}t^{\prime}} \right)}} \right)}}} \\{{\prod\limits_{\substack{q \leq t_{N + 1} \\ q \neq t}}\; \left( {1 - {F_{\Delta \; {x{({qq})}}}\left( {x - {{\hat{x}}_{pTotal}^{Kalman}\left( {qq} \right)}} \right)}} \right)}} \\{= {f_{\Delta \; {x{({t_{N + 1}t_{N + 1}})}}}\left( {x - {{\hat{x}}_{pTotal}^{Kalman}\left( {t_{N + 1}t_{N + 1}} \right)}} \right)}} \\{{{\prod\limits_{\underset{q \neq t_{N + 1}}{q \leq t_{N + 1}}}\; \left( {1 - {F_{\Delta \; {x{({qq})}}}\left( {x - {{\hat{x}}_{pTotal}^{Kalman}\left( {qq} \right)}} \right)}} \right)} +}} \\{{\sum\limits_{t^{\prime} \leq t_{N}}\; {f_{\Delta \; {x{({t^{\prime}t^{\prime}})}}}\left( {x - {{\hat{x}}_{P^{Total}}^{Kalman}\left( {t^{\prime}t^{\prime}} \right)}} \right)}}} \\{{\prod\limits_{\underset{q \neq t_{N + 1}}{q \leq t_{N + 1}}}\; \left( {1 - {F_{\Delta \; {x{({qq})}}}\left( {x - {{\hat{x}}_{pTotal}^{Kalman}\left( {qq} \right)}} \right)}} \right)}} \\{= {f_{\Delta \; {x{({t_{N + 1}t_{N + 1}})}}}\left( {x - {{\hat{x}}_{pTotal}^{Kalman}\left( {t_{N + 1}t_{N + 1}} \right)}} \right)}} \\{{{\prod\limits_{q \leq t_{N}}\; \left( {1 - {F_{\Delta \; {x{({qq})}}}\left( {x - {{\hat{x}}_{pTotal}^{Kalman}\left( {qq} \right)}} \right)}} \right)} +}} \\{{\sum\limits_{t^{\prime} \leq t_{N}}\; {f_{\Delta \; {x{({t^{\prime}t^{\prime}})}}}\left( {x - {{\hat{x}}_{P^{Total}}^{Kalman}\left( {t^{\prime}t^{\prime}} \right)}} \right)}}} \\{{\left( {1 - {F_{\Delta \; {x{({t_{N + 1}t_{N + 1}})}}}\left( {x - {{\hat{x}}_{pTotal}^{Kalman}\left( {t_{N + 1}t_{N + 1}} \right)}} \right)}} \right) \times}} \\{{\prod\limits_{\underset{q \neq r^{\prime}}{q \leq t_{N}}}\; \left( {1 - {F_{\Delta \; {x{({qq})}}}\left( {x - {{\hat{x}}_{pTotal}^{Kalman}\left( {qq} \right)}} \right)}} \right)}} \\{= {{{f_{\Delta \; {x{({t_{N + 1}t_{N + 1}})}}}\left( {x - {{\hat{x}}_{pTotal}^{Kalman}\left( {t_{N + 1}t_{N + 1}} \right)}} \right)}{\Gamma \left( {t_{N},x} \right)}} +}} \\{\left( {1 - {F_{\Delta \; {x{({t_{N + 1}t_{N + 1}})}}}\left( {x - {{\hat{x}}_{pTotal}^{Kalman}\left( {t_{N + 1}t_{N + 1}} \right)}} \right)}} \right)} \\{{{f_{\min}\left( {t_{N},x} \right)}.}}\end{matrix} & (11)\end{matrix}$

Here it is seen that the computation of an updated conditionalprobability distribution of the noise floor measure ƒ_(min)(t_(N+1),x)can be performed as a summation of two terms. A first term ƒ_(Δx(t)_(N+1) _(|t) _(N+1) ₎(x−{circumflex over (x)}_(P) _(Total)^(Kalman)(t_(N+1)|t_(N+1)))Γ(t_(N),x) is a product of the previouslycomputed product Γ(t_(N),x) of complements of the cumulative errordistribution of the first power quantity and a second factor ƒ_(Δx(t)_(N+1) _(t) _(N+1) ₎(x−{circumflex over (x)}_(P) _(Total)^(Kalman)(t_(N+1)|t_(N+1))). This second factor is as seen based on anew probability distribution for the first power quantity. The secondterm (1−F_(Δx(t) _(N+1) _(t) _(N+1) ₎(x−{circumflex over (x)}_(P)_(Total) ^(Kalman)(t_(N+1)|t_(N+1))), ƒ_(min)(t_(N),x) is a product of apreviously computed conditional probability distributionƒ_(min)(t_(N),x) of the noise floor measure and the first factor1−F_(Δx(t) _(N+1) _(|t) _(N+1) ₎(x−{circumflex over (x)}_(P) _(Total)^(Kalman)(t_(N+1)|t_(N+1))), already used in the recursive calculationof the completed product.

As a conclusion, it is seen that a recursive computing of theconditional probability distribution of the noise floor measure is basedon a previously computed conditional probability distribution of thenoise floor measure, a previously computed product of complements of apreviously computed cumulative error distribution of the first powerquantity, and a new probability distribution for the first powerquantity. The product of complements of the cumulative errordistribution of the first power quantity is also recursively computablebased on a previously computed product of complements of the cumulativeerror distribution of the first power quantity and a factor being thecomplement of a new cumulative probability distribution for the firstpower quantity. The recursive computation is in other words a coupledrecursive computation of two quantities, namely the conditionalprobability distribution of the noise floor measure itself and theproduct of complements of the cumulative error distribution of the firstpower quantity. These are the main entities which have to be stored fromone update to the next. Said main entities are discretized over the samepower grid as used by the sliding window algorithm (see Appendix B),however, the time dimension of the sliding window is removed. Areduction of the memory requirements by a factor of 100 as compared tosoft noise floor algorithm based on sliding window techniques can beachieved. This enables the use of the disclosed algorithm for loadestimation in the admission control algorithm even in the RNC.

The recursive computation can be illustrated graphically in a flow chartas in FIG. 6. 800 denotes a currently computed error distribution forthe first power quantity. A cumulative error distribution of the firstpower quantity is calculated in 801. The first factor 804, based on thecumulative error distribution, is entered into the recursive calculation802 of a product of complements together with the previously computedproduct of complements 805. The previously computed product ofcomplements 805 is also combined with a second factor 809 into a firstterm 808 for the recursive calculation 803 of the conditionalprobability distribution of the noise floor measure. The second term 807into this calculation 803 comprises the first factor 804 and apreviously calculated conditional probability distribution 806 of thenoise floor measure.

The presently proposed recursive approach involves an approximation.However, as seen in FIG. 7, the influence of this approximation isalmost negligible. The figure is a comparison between a sliding windowimplementation 700, and the recursive algorithm disclosed in the presentdocument 701. The agreement is as seen excellent. The variation is onlyabout 0.05 dB mean square. The varying behavior of the disclosedalgorithm is due to a tuning for best tracking performance.

In its basic form, the recursive approach has some disadvantages. Themost obvious one is the property of never forgetting any previousinformation completely. The algorithm will therefore converge to asteady state, and any drifts or changed conditions will have problems toinfluence the noise floor estimation after a while. It is thereforepreferable to include some sort of data forgetting mechanism.

A first simple approach to data forgetting is simply to interrupt thealgorithm and let the algorithm start up again from initial values. Thiswill allow for changes in conditions, but will decrease the performanceduring the first period after start-up. A somewhat more elaborateapproach is to let a new recursion start up a while before the old oneis stopped. In such a case, the new one may have approached the truenoise floor value better before it is actually used. The drawback isthat two parallel recursions are active for a while, which complicatesthe implementation.

Data forgetting may also be introduced by recursive discrete timefiltering, techniques, e.g. by means of a standard recursive first orderdiscrete time filter. The bandwidth of the resulting algorithm isdirectly controlled by the filter constants of the recursive filter. Foreach fixed power grid point, the recursion (11) is in a form thatimmediately lends itself to introduction of data forgetting, consideringƒ_(min)(t_(N),x) as the state and Γ(t_(N),x) as the input. Using 0<β<1as filter constant, results in the recursion:

ƒ_(min)(t _(N+1) ,x)=β(1−F _(Δx(t) _(N+1) _(|t) _(N+1) ₎(x−{circumflexover (x)} _(P) _(Total) ^(Kalman)(t _(N+1) |t _(N+1))))ƒ_(min)(t _(N),x)+(1−β)ƒ_(Δx(t) _(N+1) _(|t) _(N+1) ₎(x−{circumflex over (x)} _(P)_(Total) ^(Kalman)(t _(N+1) |t _(N+1)))Γ(t _(N) ,x).   (12)

The recursion (10) cannot be cast into linear recursive filtering formas it stands. However, by taking logarithms, the following recursion isobtained

ln(Γ(t _(N+1) ,x))=ln(1−F _(Δx(t) _(N+1) _(|t) _(N+1) ₎(x−{circumflexover (x)} _(P) _(Total) ^(Kalman)(t _(N+1) |t _(N+1))))+ln(Γ(t _(N),x)).   (13)

Data forgetting can then be introduced into (13), using the filterconstant α. The result is:

ln(Γ(t _(N+1) ,x))=(1−α)ln(1−F _(Δx(t) _(N+1) _(|t) _(N+1)₎(x−{circumflex over (x)} _(P) _(Total) ^(Kalman)(t _(N+1) |t_(N+1))))+αln(Γ(t _(N) ,x))   (14)

After exponentiation, the following geometric filtering recursion isobtained:

Γ(t _(N+1) ,x)=(1−F _(Δx(t) _(N+1) _(|t) _(N+1) ₎(x−{circumflex over(x)} _(P) _(Total) ^(Kalman)(t _(N+1) |t _(N+1))))^(1−α)Γ(t _(N),x)^(α).   (15)

The recursions (12) and (15) constitute the end result. The output fromthese coupled recursions is combined with the prior information as in(B13) of Appendix B, and the calculations proceed from there.

Initiation of (12) and (15) is obtained by putting:

Γ(t ₀ , x)=1(

Γ(t ₁ ,x)=1−F _(Δx(t) ₁ _(t) ₁ ₎(x−{circumflex over (x)} _(P) _(Total)^(Kalman)(t ₁ |t ₁))),   (16)

ƒ_(min)(t ₀ ,x)=0(

ƒ_(min)(t ₁ ,x)=ƒ_(Δx(t) ₁ _(|t) ₁ ₎(x−{circumflex over (x)} _(P)_(Total) ^(Kalman)(t ₁ |t ₁))),   (17)

which is the correct initial behavior.

There are also other ways to introduce data forgetting. One possibilityis to use stochastic propagation of the probability density function of(11). This then would require a dynamic model assumption for thediffusion of the probability density function. The approach is fairlycomplicated and is not treated in detail here.

The introduction of recursive algorithms for soft noise floor estimationhas several advantages. One advantage is that the algorithms requireonly approximately 0.005 Mbyte of memory per cell, i.e. about 1%compared with sliding window approaches. The recursive algorithms reducethe computational complexity further, also as compared to the slidingwindow algorithms. They avoid the need for control of the computationalcomplexity, with parameter constraints, thereby also reducing the numberof parameters for management significantly. They also allow tuning byconsideration of standard engineering bandwidth considerations, usingalpha and beta tuning parameters.

The tracking properties of the recursive algorithms can be furtherimproved. A specific handling of certain threshold parameters can beintroduced to obtain good tracking properties over very wide dynamicranges.

To explain the first addition, note that during iteration, the values ofthe probability density function of the minimum power can become verysmall in grid points well above the wideband power measured in a cell.It can even be 0 to within the resolution of the computer arithmetic.This is acceptable as long as the thermal noise floor does not change.However, in case the thermal noise power floor would suddenly increase,very small values of the probability density function that fall belowthe measured wideband power after the noise floor change, will require avery long time to grow until they become close to 1. As a consequence,the tracking ability will be poor in case the noise floor wouldincrease. Actual changes can thereby take very long times before beingnoticed at all.

In order to counteract this unwanted behavior, a minimum permitted valueof the probability density function of the minimum power is introduced.Any calculation of a smaller value will be exchanged to the minimumvalue. Typically, a value around 0.000001 has been found to be suitable.

The tracking performance of the proposed algorithm has beeninvestigated. As can be seen in FIG. 8, the algorithm successfullytracks the thermal noise power floor over 50 dB. The power change isintroduced at 1000 s.

The probability density function of the noise floor is illustrated inFIG. 9. All values of the probability density function are changed to0.000001 if a smaller value is obtained in the calculations. The showncase illustrates a situation where the probability density function isincreasing around −75 dBm 600 while it is decreasing around −110 dBm,601. Even though the probability density function is larger around −110dBm, 601, the probability density function around −75 dBm, 600,dominates the conditional mean of the probability distribution, whichequals the optimum estimate. This is because −75 dBm, 600, is a muchlarger power and since the peak around −110 dBm, 601, is narrower. Note:The peak around −110 dBm, 601, is a remnant from an initial period oftime that will eventually disappear.

However, a consequence of the above change is that an unwanted bias isintroduced, when the estimate of the thermal noise power floor isestimated. The origin of said bias is the artificially high values ofthe probability density function of the minimum power that is normallyintroduced in the majority of the grid points. These high values resultin domination by high power grid points in the conditional mean, a factthat manifests itself in a too high estimated noise power floor.

Fortunately this latter problem can be taken care of, simply by removingpower grid points that are at the minimum level from all computations ofthe conditional mean. In other words, for the purpose of estimating thethermal noise power floor, the grid points falling below the minimumvalue are instead set to identically zero. Note that this should also beapplied when a soft noise rise estimate is computed using a quotientdistribution.

The algorithmic additions enable tracking over more than 50 dBs of inputpower. This in turn makes it possible to efficiently handle erroneouslyconfigured RBSs that occur frequently in WCDMA networks. Sucherroneously configured RBSs may see artificial noise floors between −120dBm and −70 dBm. Furthermore, one can avoid the need for the safety netsthat are required for various sliding window algorithms. These safetynets introduce logic for further control of the estimated thermal noisefloor.

It is stressed that there is a strong operational need for efficientload estimation in the admission control function of the RNC. Due toconfiguration errors in the RBSs and the front end scale factor errors,a substantial amount of manual work is presently needed for admissioncontrol algorithm tuning, in fielded systems.

An important advantage is that the algorithm disclosed in the presentinvention disclosure lends itself to ASIC implementation. This is due tothe fact that the algorithm operates as a recursive filter, with no needfor dynamic memory allocation. This fact makes the proposed algorithmsuitable for a replacement of presently intended sliding window basedalgorithm of the RBS, in later RBS releases if this should be deemedcost efficient.

In the description above, it is assumed that the power estimationsconcern uplink communication. The power measurements are in such casesperformed by a node in the radio access network, typically the radiobase station or the radio network controller. FIG. 10 illustrates mainparts of an embodiment of a system according to the present invention,where load estimation is performed in the RNC. A wireless communicationssystem 170 comprises a Universal mobile telecommunication systemTerrestrial Radio Access Network (UTRAN) 171. A mobile terminal 25 is inradio contact with a RBS 20 in the UTRAN 171. The RBS 20 is controlledby a Radio Network Controller (RNC) 172, which in turn is connected to aMobile services Switching Centre/Visitor Location Register (MSC/VLR) 174and a Serving General packet radio system Support Node (SGSN) 175 of acore network CN 173.

In this embodiment, the RBS 20 comprises a power sensing arrangement 51,typically an antenna and front end electronics, for measuringinstantaneous received total wideband power. A connection 53, theso-called Iub interface, is used for communication between the RBS 20and the RNC 172. According to standards, the Iub interface allows fortransferring measured samples of received total wideband power. Theconnection 53 is thus a means for the RNC 172 to obtain datarepresenting measured samples of received total wideband power. A noiserise estimation arrangement 50 is available in the RNC 172, arranged forreceiving measured samples of received total wideband power over theconnection 53.

FIG. 11 illustrates a flow diagram of main steps of an embodiment of amethod according to the present invention. The procedure starts in step200. In step 202, a number of samples of at least the received totalwideband power are measured. In step 210, a probability distribution fora first power quantity is estimated from at least the measured samplesof the received total wideband power. The first power quantity can bethe received total wideband power. In step 214, a conditionalprobability distribution of a noise floor measure is computed based onat least the probability distribution for the first power quantity. Thisstep is performed recursively. Finally, in step 218, a value of a noiserise measure is calculated based at least on the conditional probabilitydistribution for the noise floor measure. The procedure ends in step299.

The embodiments described above are to be understood as a fewillustrative examples of the present invention. It will be understood bythose skilled in the art that various modifications, combinations andchanges may be made to the embodiments without departing from the scopeof the present invention. In particular, different part solutions in thedifferent embodiments can be combined in other configurations, wheretechnically possible. The scope of the present invention is, however,defined by the appended claims.

Appendix A Kalman Filter for RTWP Measurements

A proposed algorithm for the case where the total RTWP is measured is aprediction-update filter, where the subscripts distinguish between theprediction and the update steps.

$\begin{matrix}{\mspace{79mu} {{K_{Update}(t)} = \frac{P_{Prediction}^{Cov}\left( {t - T_{\min}} \right)}{{P_{Prediction}^{Cov}\left( {t - T_{\min}} \right)} + r_{Measurement}}}} & \left( {A\; 1} \right) \\{{P_{Update}^{Total}(t)} = {{P_{Prediction}^{Total}\left( {t - T_{\min}} \right)} + {{K_{Update}(t)} \times \begin{pmatrix}{{P_{Measurement}^{Total}(t)} -} \\{P_{Prediction}^{Total}(t)}\end{pmatrix}}}} & \left( {A\; 2} \right) \\{\mspace{79mu} {{P_{Update}^{Cov}(t)} = {{P_{Prediction}^{Cov}\left( {t - T_{\min}} \right)} - \frac{P_{Prediction}^{{Cov}^{2}}\left( {t - T_{\min}} \right)}{\begin{matrix}{{P_{Prediction}^{Cov}\left( {t - T_{\min}} \right)} +} \\r_{Measurement}\end{matrix}}}}} & \left( {A\; 3} \right) \\{\mspace{79mu} {{P_{Prediction}^{Total}(t)} = {P_{Update}^{Total}(t)}}} & \left( {A\; 4} \right) \\{\mspace{79mu} {{P_{Prediction}^{Cov}(t)} = {{P_{Update}^{Cov}(t)} + {\frac{T_{\min}}{T_{Correlation}}r}}}} & \left( {A\; 5} \right)\end{matrix}$

(A1)-(A5) are repeated increasing t by steps of T_(min).

Initialization is made at t=0 by:

P _(Prediction) ^(Total)(0)=P ₀ ^(Total)   (A6)

P _(Prediction) ^(Cov)(0)=P ₀.   (A7)

As seen above, the updating gain K_(Update)(t) is computed from themodel parameter r_(Measurement) and from a predicted covarianceP_(Prediction) ^(Cov)(t−T_(min)) obtained at the previous samplinginstance. The total wideband power updated with the latest measurementP_(Update) ^(Total)(t) is then computed, using the predictionP_(Prediction) ^(Total)(t) and the new measurement P_(Measurement)^(Total)(t). The next step is to compute the updated covarianceP_(Update) ^(Cov)(t) from the predicted covariance and fromr_(Measurement). In the final steps of iteration new values ofP_(Prediction) ^(Total)(t) and P_(Prediction) ^(Cov)(t) are calculatedand the time is stepped. T_(min) denotes the sampling period.

Appendix B

Estimation of the conditional probability distribution of

$\min\limits_{t^{\prime} \in {\lbrack{{t - T_{Lag}},t}\rbrack}}{P^{Total}\left( t^{\prime} \right)}$

Note: It is very natural to estimate minimum powers. However, the choiceto use the minimum value is really ad-hoc. In a general case, an extremevalue of a quantity in some way dependent on the estimated P^(Total)quantity would be possible to use as a base for the furthercomputations. However, as a simplest embodiment the quantity

$\min\limits_{t^{\prime} \in {\lbrack{{t - T_{Lag}},t}\rbrack}}{P^{Total}\left( t^{\prime} \right)}$

is considered here. Note that P^(Total) in the coming discussion refersto the received total wideband power.

Notation, Conditional Probability and Baye's Rule

In the following Bayes rule and the definition of conditional mean, forprobability distributions, are used extensively. The followingdefinitions and results can be found e.g. in T. Söderstrom, DiscreteTime Stochastic Systems. London, UK: Springer, 2002, pages 12-14, or anyother text book on estimation.

Probability distributions: Consider two events A and B, with probabilitydistributions ƒ_(A)(x) and ƒ_(B)(y), respectively. Then the jointprobability distribution of A and B is denoted f_(A,B)(x,y).

Note that the events and the conditioning are expressed by subscripts,whereas the independent variables appear within the parentheses. Thisnotation is used only when probability distributions and cumulativeprobability distributions are used. When state estimates andcovariances, e.g. of the Kalman filter, are referred to, theconditioning may also appear within parentheses.

Conditional probability distributions: The conditional probabilitydistributions ƒ_(AB)(x) and ƒ_(B|A)(y) are defined by:

ƒ_(A,B)(x,y)=ƒ_(A|B)(x)ƒ_(B)(y)=ƒ_(B|A)(y)ƒ_(A)(x).   (B1)

Note that as a consequence of the notation for probabilitydistributions, also the conditioning is expressed as subscripts.

A solution of the above equation now results in the famous Bayes rule:

$\begin{matrix}{{f_{AB}(x)} = {\frac{{f_{BA}(y)}{f_{A}(x)}}{f_{B}(y)}.}} & \left( {B\; 2} \right)\end{matrix}$

Note that the rules above are best understood by using intersectingcircle diagrams. The formal proofs to obtain the results for probabilitydistributions can e.g. use infinitesimal limiting versions ofmotivations for the probability cases.

Conditional Probability of the Minimum—Model and General Expressions

In this section some general properties of a minimum estimator arederived. Towards that end, the following notation is introduced. TheKalman filter or Kalman smoother estimate of P^(Total)(t′) is denotedby:

$\begin{matrix}\begin{matrix}{{{\hat{x}}_{P^{Total}}^{Kalman}\left( {t^{\prime}Y^{t}} \right)} \equiv {{\hat{x}}_{P^{Total}}^{Kalman}\left( {t^{\prime}\left\{ {y(s)} \right\}_{N \in {\lbrack{{- \infty},t}\rbrack}}} \right)}} \\{= {{\hat{x}}_{P^{Total}}^{Kalman}\left( {t^{\prime}\left\{ {y(s)} \right\}_{{S \in {\lbrack{{t - T_{Lag}},t}\rbrack}},}} \right.}} \\{\left. {{\hat{x}}_{P^{Total}}^{Kalman}\left( {{t - T_{Lag}}Y^{t - T_{Lag}}} \right)} \right).}\end{matrix} & \left( {B\; 3} \right)\end{matrix}$

Here t′ denotes some time within └t−T_(Log),t┘. The conditionaldistributions are, under mild conditions, all Gaussian sufficientstatistics, i.e. only second order properties are needed in order todescribe the conditional probability distributions. This is reflected inthe conditioning in the last expression of (A3). The conditionaldistributions follow as:

ƒ_({circumflex over (x)}) _(P) ^(Total) ^(Kalman)_((t′)N′)(x)∈N({circumflex over (x)} _(P) _(Total)^(Kalman)(t′|t),(σ_(P) _(Total) ^(Kalman)(t′|t))²),   (B4)

where ^(Kalman) indicates that the estimate is computed with the Kalmanfilter or, if t′<t, the Kalman smoother. The quantities {circumflex over(x)}_(P) ^(Total) ^(Kalman)(t′|t) and (σ_(P) _(Total) ^(Kalman)(t′|t))²denote the power estimate and the corresponding covariance,respectively, i.e. the inputs to the estimator. Note that (B4) assumesthat the corresponding estimate at time t−T_(Log) is used as initialvalue for the Kalman filter.

Then the conditional distribution for the minimum value of the powerestimate can be further developed. Towards that end the following modelis assumed for the relation between x_(P) _(Total) ⁰(t′)=P^(0,Total)(t′)that represents the true power and {circumflex over (x)}_(P) _(Total)^(Kalman)(t′|t)={circumflex over (P)}^(Total)(t′|t) that represents theestimate:

x _(P) _(Total) ⁰(t′)={circumflex over (x)} _(P) _(Total)^(Kalman)(t′|t)+Δx _(P) _(Total) (t′|t)   (B5)

x _(P) _(Total) ⁰(t′)∈N({circumflex over (x)} _(P) _(Total)^(Kalman)(t′|t), (σ_(P) _(Total) ^(Kalman))(t′|t))²).   (B6)

This is in line with the above discussion on sufficient statistics. Thenotation for the distribution of Δx_(P) _(Total) (t′|t) is henceforwardsimplified to:

ƒ_(Δx)(x).   (B7)

Note that this distribution does not have to be assumed to be Gaussian(although this is mostly the assumption made).

The conditional probability distribution of the minimum value of x_(P)_(Total) ⁰(t′)=P^(0,Total)(t′), t′∈└t−T_(Log),t┘ is then to be estimatedusing data y(t), obtained from the time interval [−∞,t].

FIG. 4 illustrates a diagram showing time variations 102 of a totalreceived wideband power P^(Total)(t). During some time intervals, thetotal received wideband power presents high values. However, at someoccasions, the total received wideband power becomes small, indicatingthat many of the usual contributions to the measured power are absent.

As will be seen below, smoother estimates are theoretically required asinputs to the conditional probability estimation algorithm for theminimum power that operates over the time interval └t−T_(Log),t┘. Toformally retain optimality in the development, the smoother estimatesshould also be calculated using all data in └t−T_(Log),t┘. However, in apractical implementation, these smoother estimates are typicallycomputed using only a short snapshot of data around the selectedsmoothing time instance. Several such smoothing estimates, from└t−T_(Log),t┘, are then combined to estimate the conditional probabilitydistribution. In the coming discussion the interval └t−T_(Log),t┘ isretained in all quantities though, so as not to complicate thedevelopment too much. A further simplification can be obtained byreplacement of the smoother estimate with a Kalman filter estimate.Simulations indicate that this can be done with very little loss ofperformance.

The conditional distribution of the minimum value can now be written asfollows (cf. (BS)):

ƒ_(min{x) _(ptotal) ⁰ _((t′)}) _(r=|r−TLog t )) |Y′, min x _(pTotal) ⁰_((t−T) _(Log) ₎(x),   (B8)

where the last quantity of (B8) denotes the initial information of theminimum value. In the following Bayes rule and the definition ofconditional mean, for probability distributions, are used extensively.

Then apply Bayes rule and the definition of conditional probability to(B8) using the definitions:

A:=min{x _(P) _(Total) ⁰(t′)}_(t′∈└t−T) _(Log) _(,t┘)

B:=minx _(P) _(Total) _((t−t) _(Log) ₎ ⁰

C:=Y′

The following chain of equalities then holds, using Bayes rule, thedefinition of conditional probability distributions, and the resultƒ_(B,C|A)(x,y)=ƒ_((B|A),(C|A))(x,y) (the latter result is easily checkedby the drawing of a three-circle diagram):

$\begin{matrix}\begin{matrix}{{f_{AB}(x)} = \frac{{f_{B,{CA}}\left( {x,y} \right)}{f_{A}(x)}}{f_{B,C}\left( {x,y} \right)}} \\{= \frac{{f_{{({BA})},{({CA})}}\left( {x,y} \right)}{f_{A}(x)}}{f_{B,C}\left( {x,y} \right)}} \\{= \frac{{f_{{({BA})}{({CA})}}(x)}{f_{CA}(y)}{f_{A}(x)}}{f_{B,C}\left( {x,y} \right)}} \\{= \frac{{f_{{BA},C}(x)}{f_{CA}(y)}{f_{A}(x)}}{f_{B,C}\left( {x,y} \right)}} \\{= {\frac{{f_{{BA},C}(x)}{f_{AC}(x)}{f_{C}(y)}}{f_{B,C}\left( {x,y} \right)}.}}\end{matrix} & \left( {B\; 9} \right)\end{matrix}$

The last step can again be easily verified by drawing circle diagrams.Now, according to the definitions above, the first factor of thenumerator of (B9) is a prior and hence the conditioning disappears. Thesecond factor of the numerator will be further expanded below, whereasthe last factor of the numerator and the denominator can be treated asparts of a normalizing constant. Back-substitution of the definitions ofA, B and C then proves the relation:

$\begin{matrix}{{f_{{{\min {\{{x_{P^{Total}}^{0}{(t^{\prime})}}\}}_{r \in {\lbrack{{t - T_{Lag}},t}\rbrack}}}Y^{t}},{\min \; {x_{P^{Total}}^{0}{({t - T_{Lag}})}}}}(x)} = {\frac{1}{c}{f_{{\min {\{{x_{P^{Total}}^{0}{(t^{\prime})}}\}}_{t^{\prime}\; \in {\lbrack{{t - T_{Lag}},t}\rbrack}}}Y^{t}}(x)}{{f_{\min \; {x_{P^{Total}}^{0}{({t - T_{Lag}})}}}(x)}.}}} & \left( {B\; 10} \right)\end{matrix}$

One consequence of (B10) that needs to be kept in mind is that asmoothing problem is at hand. The Kalman filtering based pre-processingstep treated above hence formally needs to include a Kalman smootherstep. In practice, the Kalman filter is normally sufficient though.

Final Expansion of the Conditional Mean of the Minimum Power

The starting point of this subsection is equation (B10) that states thatthe conditional pdf (probability distribution function) is given as theproduct of a prior (initial value) and a measurement dependant factor.The prior is supplied by the user and should reflect the prioruncertainty regarding PN. Note that whenever the sliding window is movedand a new estimate is calculated, the same prior is again applied. Theprior is hence not updated in the basic setting of the estimator.

To state the complete conditional pdf some further treatment of thefirst factor of (B10) is needed. The error distribution ƒ_(ΔP)(x) of(B7), together with the definitions (B5) and (B6) will be centraltowards this end. Further, in the calculations below, F( ) denotes acumulative distribution, i.e. the integral of ƒ. Pr(.) denotes theprobability of an event.

The following equalities now hold for the first factor of (B10):

$\begin{matrix}{{F_{{\min {\{{x_{P^{Total}}^{0}{(t^{\prime})}}\}}_{t^{\prime} \in {\lbrack{{t - T_{Lag}},t}\rbrack}}}Y^{\prime}}(x)} = {{\Pr \left( {{{\min \left\{ {x_{P^{Total}}^{0}\left( t^{\prime} \right)} \right\}_{t^{\prime} \in {\lbrack{{t - T_{Lag}},t}\rbrack}}} \leq x}Y^{t}} \right)} = {{1 - {\Pr \left( {{{\min \left\{ {x_{P^{Total}}^{0}\left( t^{\prime} \right)} \right\}_{t^{\prime} \in {\lbrack{{t - T_{Lag}},t}\rbrack}}} > x}Y^{t}} \right)}} = {{1 - {\Pr \left( {{\forall t^{\prime}},{{\Delta \; {x_{P^{Total}}\left( {t^{\prime}t} \right)}} > {x - {{\hat{x}}_{P^{Total}}^{Kalman}\left( {t^{\prime}t} \right)}}}} \right)}} = {{1 - {\prod\limits_{t^{\prime} \in {\lbrack{{t - T_{Lag}},t}\rbrack}}\; {\Pr \left( {{\Delta \; {x_{P^{Total}}\left( {t^{\prime}t} \right)}} > {x - {{\hat{x}}_{P^{Total}}^{Kalman}\left( {t^{\prime}t} \right)}}} \right)}}} = {{1 - {\prod\limits_{t^{\prime} \in {\lbrack{{t - T_{Lag}},t}\rbrack}}\left( {1 - {\Pr \left( {{\Delta \; {x_{P^{Total}}\left( {t^{\prime}t} \right)}} \leq {x - {{\hat{x}}_{P^{Total}}^{Kalman}\left( {t^{\prime}t} \right)}}} \right)}} \right)}} = {1 - {\prod\limits_{t^{\prime} \in {\lbrack{{t - T_{Lag}},t}\rbrack}}\; {\left( {1 - {F_{\Delta \; {x{({t^{\prime}t})}}}\left( {x - {{\hat{x}}_{P^{Total}}^{Kalman}\left( {t^{\prime}t} \right)}} \right)}} \right).}}}}}}}}} & \left( {B\; 11} \right)\end{matrix}$

The fourth equality of (B11) follows from the assumption that the Kalmansmoother provides a sufficient statistics, i.e. (B5) and (B6). The lastequality follows from (B7). Obviously, the most natural assumption is touse a Gaussian distribution for F_(ΔP(s)). However, (B11) actuallyallows other distributions as well.

The final step in the derivation of the first factor of the distributionfunction is to differentiate (B11), obtaining:

$\begin{matrix}\begin{matrix}{{f_{{\min {\{{x_{P^{Total}}^{0}{(t^{\prime})}}\}}_{t^{\prime} \in {\lbrack{{t - T_{Lag}},t}\rbrack}}}Y^{t}}(x)} = \frac{{F_{{\min {\{{x_{P^{Total}}^{0}{(t^{\prime})}}\}}_{t^{\prime} \in {\lbrack{{t - T_{Lag}},t}\rbrack}}}Y^{\prime}}(x)}}{x}} \\{= {\sum\limits_{t^{\prime} \in {\lbrack{{t - T_{Lag}},t}\rbrack}}\; {f_{\Delta \; {x{({t^{\prime}t})}}}\left( {x - {{\hat{x}}_{P^{Total}}^{Kalman}\left( {t^{\prime}t} \right)}} \right)}}} \\{{\prod\limits_{\underset{q \neq t^{\prime}}{q \in {\lbrack{{t - T_{Lag}},t}\rbrack}}}\; \begin{pmatrix}{1 - F_{\Delta \; {x{({t^{\prime}t})}}}} \\\left( {x - {{\hat{x}}_{P^{Total}}^{0}\left( {t^{\prime}q} \right)}} \right)\end{pmatrix}}}\end{matrix} & \left( {B\; 12} \right)\end{matrix}$

Combining with (B10), gives the end result:

$\begin{matrix}{{f_{{{\min {\{{x_{P^{Total}}^{0}{(t^{\prime})}}\}}_{t^{\prime} \in {\lbrack{{t - T_{Lag}},t}\rbrack}}}Y^{t}},{\min \; {x_{P^{Total}}^{0}{({t - T_{Lag}})}}}}(x)} = {\frac{1}{c}\begin{pmatrix}{\sum\limits_{t^{\prime} \in {\lbrack{{t - T_{Lag}},t}\rbrack}}\; {f_{\Delta \; {x{({t^{\prime}t})}}}\left( {x - {{\hat{x}}_{P^{Total}}^{Kalman}\left( {t^{\prime}t} \right)}} \right)}} \\{\prod\limits_{\underset{q \neq t^{\prime}}{q \in {\lbrack{{t - T_{Lag}},t}\rbrack}}}\; \left( {1 - {F_{\Delta \; {x{({t^{\prime}t})}}}\left( {x - {{\hat{x}}_{P^{Total}}^{Kalman}\left( {t^{\prime}q} \right)}} \right)}} \right)}\end{pmatrix}{f_{\min \; {x_{P^{Total}}^{0}{({t - T_{Lag}})}}}(x)}}} & \left( {B\; 13} \right)\end{matrix}$

This result constitutes the output 79 referred to in connection withFIG. 5. The expression may look complex. It is fortunatelystraightforward to evaluate since it is a one dimensional function ofGaussian and cumulative Gaussian distributions given by:

$\begin{matrix}{{f_{\Delta \; {x{({t^{\prime}t})}}}\left( {x - {{\hat{x}}_{P^{Total}}^{Kalman}\left( {t^{\prime}t} \right)}} \right)} = {\frac{1}{\sqrt{2\; \pi}{\sigma_{P^{Total}}^{Kalman}\left( {t^{\prime}t} \right)}}^{- \frac{{({x - {{\hat{x}}_{P^{Total}}^{Kalman}{({t^{\prime}t})}}})}^{2}}{2{({\sigma_{P^{Total}}^{Kalman}{({t^{\prime}t})}})}^{2}}}}} & \left( {B\; 14} \right) \\\begin{matrix}{{F_{\Delta \; {x{({t^{\prime}t})}}}\left( {x - {{\hat{x}}_{P^{Total}}^{Kalman}\left( {t^{\prime}t} \right)}} \right)} = {\int_{- \infty}^{x - {{\hat{x}}_{P^{Total}}^{Kalman}{({t^{\prime}t})}}}{{f_{{\Delta \; {r{({t^{\prime}t})}}}\;}(y)}\ {y}}}} \\{= {\frac{1}{2}{{{erfc}\left( {- \frac{\left( {x - {{\hat{x}}_{P^{Total}}^{Kalman}\left( {t^{\prime}t} \right)}} \right)}{\sqrt{2}{\sigma_{P^{Total}}^{Kalman}\left( {t^{\prime}t} \right)}}} \right)}.}}}\end{matrix} & \left( {B\; 15} \right)\end{matrix}$

The quantities {circumflex over (x)}_(P) _(Total) ^(Kalman)(t′|t) andσ_(P) _(Total) ^(Kalman)(t′|t) are readily available as outputs from theKalman smoother, or the simpler Kalman filter.

If a noise floor value is to be provided as an output, a mean valuecomputation is performed on the output distribution.

1. Method for noise rise estimation in a wireless communications system,comprising the steps of: measuring samples of at least received totalwideband power; estimating a probability distribution for a first powerquantity from at least said measured samples of at least received totalwideband power; computing a conditional probability distribution of anoise floor measure based on at least said probability distribution forsaid first power quantity; said step of computing being performedrecursively; and calculating a value of a noise rise measure based onsaid conditional probability distribution for said noise floor measure.2. The method according to claim 1, wherein said recursive computing ofsaid conditional probability distribution of said noise floor measure isbased on a previously computed conditional probability distribution ofsaid noise floor measure, a previously computed product of complementsof a previously computed cumulative error distribution of said firstpower quantity and a new probability distribution for said first powerquantity.
 3. The Method according to claim 2, wherein said recursivecomputing of said conditional probability distribution of said noisefloor measure is based on a recursive computing of said computed productof complements of a previously computed cumulative error distribution ofsaid first power quantity.
 4. The method according to claim 3, whereinsaid step of recursively computing said conditional probabilitydistribution of said noise floor measure in turn comprises the steps of:computing a present product of complements of said cumulative errordistribution of said first power quantity as a product of a previouslycomputed product of complements of said cumulative error distribution ofsaid first power quantity and a first factor based on a new complementof said cumulative probability distribution for said first powerquantity; and computing said conditional probability distribution ofsaid noise floor measure as a sum of a first term and a second term,said first term being a product of said previously computed product ofcomplement of said cumulative error distribution of said first powerquantity and a second factor based on a new probability distribution forsaid first power quantity, said second term being a product of saidpreviously computed conditional probability distribution of said noisefloor measure and a said first factor.
 5. The method according to claim4, wherein said step of recursively computing said conditionalprobability distribution of said noise floor measure is performedaccording to: where tN is a measuring time of sample N of at least thereceived total wideband power, x denotes discretized power, is aprobability density function of a minimum of said first power quantityat time is said product of complements of said cumulative errordistribution of said first power quantity, is an error distribution ofsaid first power quantity at time +1 and is a cumulative errordistribution of said first power quantity at time Λ,+1.
 6. The methodaccording to claim 1, comprising the further step of introducing a dataforgetting mechanism.
 7. The method according to claim 6, wherein saidstep of introducing a data forgetting mechanism comprises intermittentrestarting of said noise rise estimation.
 8. The method according toclaim 6, wherein said step of introducing a data forgetting mechanismcomprises stochastic propagation of said conditional probability densityfunction of the noise floor measure.
 9. The method according to claim 4,wherein said data forgetting mechanism is implemented with filterconstants in the recursive computing steps.
 10. The method according toclaim 5, wherein said data forgetting mechanism is implemented as: r(,,.)=(i−F x−xltT(, , I v)f′{t N,_(X))°,+(i−/?)/t)(_(X)−_(X)r(t+\t+)y(t._(X)), where a and β are filterconstants.
 11. The method according to claim 1, comprising the furtherstep of introducing a minimum value of said conditional probabilitydistribution of said noise floor measure.
 12. The method according toclaim 11, wherein said minimum value is in the order of magnitude of0.000001.
 13. The method according to claim 11, wherein power gridpoints of said conditional probability distribution of said noise floormeasure having said minimum value are removed from said step ofcalculating a value of a noise rise measure based on said conditionalprobability distribution for said noise floor measure.
 14. The methodaccording to claim 1, wherein said step of calculating a value of anoise rise measure is based on an estimate of a noise floor, in turnbased on said conditional probability distribution of said noise floormeasure.
 15. The method according to claim 1, wherein said step ofcalculating a value of a noise rise measure is based on a conditionalprobability distribution of said noise rise measure, in turn based onsaid conditional probability distribution of said noise floor measure.16. The method according to claim 1, wherein said first power quantityis received total wideband power.
 17. Node of a wireless communicationssystem, comprising: means for obtaining measured samples of at leastreceived total wideband power; means for estimating a probabilitydistribution for a first power quantity from at least said measuredreceived samples of at least total wideband power, connected to saidmeans for obtaining measured samples of at least received total widebandpower; means for computing a conditional probability distribution of anoise floor measure based on at least said probability distribution forsaid first power quantity, connected to said means for estimating aprobability distribution for a first power quantity; said means forcomputing a conditional probability distribution of a noise floormeasure being arranged for performing said computing recursively; andmeans for calculating a value of said noise rise measure based on saidconditional probability distribution for said noise floor measure,connected to said means for computing a conditional probabilitydistribution of a noise floor measure.
 18. The node according to claim17, wherein said means for obtaining measured samples of received totalwideband power comprises means for receiving data representing measuredsamples of at least received total wideband power over a communicationinterface.
 19. The node according to claim 17, wherein said node is aradio network controller.
 20. The node according to claim 17, whereinsaid node is a node of a WCDMA system.
 21. (canceled)